Setup for new walk through.

Author: bsamseth

tictacnet.py

@@ -10,57 +10,4 @@ def move_accuracy(y_test, y_pred):
     """A predicted move is correct if the largest output is 1 in the test vector."""
     return np.mean(y_test[y_pred == np.max(y_pred, axis=1, keepdims=True)])
 
-
 np.random.seed(1234)
-
-df = pd.read_csv("tictactoe-data.csv")
-print("Scores:", Counter(df["score"]))
-
-# Input is all the board features (2x9 squares) plus the turn.
-X = df.iloc[:, list(range(18)) + [-2]]
-
-# Target variables are the possible move squares.
-moves = df.iloc[:, list(range(18, 27))]
-# To predict score instead, use this as the target:
-# score = pd.get_dummies(df['score'])
-
-X_train, X_test, y_train, y_test = train_test_split(X, moves, test_size=0.2)
-
-print("Train/test shapes:", X_train.shape, X_test.shape, y_train.shape, y_test.shape)
-
-model = tf.keras.Sequential()
-model.add(tf.keras.layers.Dense(128, activation="relu", input_dim=X.shape[1]))
-model.add(tf.keras.layers.Dropout(0.3))
-model.add(tf.keras.layers.Dense(64, activation="relu"))
-model.add(tf.keras.layers.Dropout(0.3))
-model.add(tf.keras.layers.Dense(32, activation="relu"))
-model.add(tf.keras.layers.Dropout(0.3))
-model.add(tf.keras.layers.Dense(moves.shape[1], activation="softmax"))
-
-# For a multi-class classification problem
-model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
-
-print(model.summary())
-
-# This is not needed, but lets you view a lot of useful information using
-# > tensorboard --logdir logs
-# at your terminal prompt.
-tensorboard_callback = tf.keras.callbacks.TensorBoard(
-    log_dir="./logs",
-    histogram_freq=1,
-    batch_size=100,
-    write_graph=True,
-    write_grads=True,
-    write_images=True,
-)
-model.fit(
-    X_train,
-    y_train,
-    epochs=100,
-    batch_size=16,
-    validation_data=[X_test, y_test],
-    callbacks=[tensorboard_callback],
-)
-
-print("accuracy:", model.evaluate(X_test, y_test))
-print("Custom accuracy:", move_accuracy(y_test.values, model.predict(X_test)))